# Analyst Agents

Agents built for research and analysis workflows — gathering sources, extracting evidence, synthesising and citing.

**Rests on:** Agentic Retrieval, Agent Loop

*AI Agents · AI Agents*


## Why learn this

This is worth your time because it reveals how modern software moves from guessing answers to proving them. You will learn how to build systems that gather information, verify facts against original sources, and produce reports where every conclusion includes a traceable link to its evidence. This capability is critical in any professional context where accuracy matters—such as financial reporting, legal discovery, or medical reviews—where being 'mostly right' is not enough and the origin of every claim must be auditable.


## The intuition

Imagine you need to answer a complex business question: 'What were the main risks mentioned in the latest quarterly earnings calls for the top five tech companies?' A standard assistant might give you a generic summary. An analyst agent works differently. It acts as a digital research assistant that refuses to guess. First, it identifies the specific documents it needs to read—for example, it locates the public transcripts for Apple, Microsoft, Google, Amazon, and Meta. It then reads each document carefully, ignoring irrelevant chat or filler. It extracts only the sentences where executives discuss risks, such as supply chain delays or regulatory changes. Next, it combines these separate findings into a single, coherent report. Crucially, for every statement in that report—for instance, 'Apple cited rising component costs'—it attaches a direct citation pointing to the exact page and paragraph in Apple’s transcript. This traceability is the core feature. You can click the link, open the original document, and verify the claim yourself. The system is designed so that a human expert reviews these citations before any final decision is made, ensuring the agent has not hallucinated or misunderstood the context.


## An analogy

Think of an analyst agent as a diligent junior legal associate working on a due diligence review. The partner (you) asks, 'Are there any litigation risks in this target company's history?' The associate does not rely on their general knowledge of law. Instead, they go to the public court records archive. They read hundreds of case files, highlighting every mention of lawsuits involving the company. They write a memo summarizing these cases, but every sentence in the summary is footnoted with a citation like 'Case No. 12345, District Court, p. 14.' The partner then reads the memo and checks the footnotes against the original court filings to confirm the associate did not miss any details or misinterpret the language. Where the analogy ends: the associate can be asked, 'Why did you exclude the case from last Tuesday?' and might explain their reasoning; an agent agent simply follows its programmed instructions to find and cite, lacking the human ability to intuitively judge which obscure precedent is truly relevant without explicit direction.


## Definition

An analyst agent is an automated system that collects data from various sources, identifies key evidence within those sources, combines that evidence into a summary or report, and provides verifiable citations for every claim it makes.


## How it connects

This concept connects to the broader category of AI Agents, which are systems capable of taking actions in the digital world rather than just generating text. It also relates to Knowledge Retrieval Systems, which are the underlying infrastructure that allows these agents to search databases and documents efficiently to find the sources they need.


## Common misconceptions

**Analyst agents just search the web and summarise.** — An analyst agent is just a chatbot with a search button.

Search is only one tool. The agent also extracts evidence, synthesises multiple sources, and formats citations. It's a whole workflow, not just a query. In fact: An analyst agent orchestrates a multi-step process: it plans what to look for, uses tools to gather, reads and extracts, synthesises, and cites — all in a loop until the task is done.

**Citations mean the answer is correct.** — If the agent cites a source, the fact is true.

The source itself could be wrong, biased, or misinterpreted. A citation only shows where the claim came from, not that it's accurate. In fact: Citations make the output checkable — they let the human reviewer verify the source and judge its quality. Trust still requires human judgement.

**Analyst agents are fully autonomous.** — The agent works without any human involvement.

In high-stakes analysis, a human is always in the loop. The agent gathers and suggests, but a human reviews and approves before the output is used. In fact: Human review is a deliberate part of the design. The agent's work is a draft, not a final decision.

**More sources always make a better analysis.** — The more documents the agent reads, the more reliable the output.

Including irrelevant or low-quality sources can crowd out the good ones and make the synthesis muddled. In fact: An analyst agent should be selective — it needs to identify the most relevant and reliable sources, not just accumulate everything.


## In short

Analyst agents are research assistants that gather sources, extract evidence, and produce cited syntheses. The core value is provenance: every claim must trace back to a source, so a human can verify. Human review is the safety gate. If you remember nothing else, remember that an analyst agent's output is only as trustworthy as its citations and its human reviewer.


## Where this is used

Analyst agents are used across many fields. In finance, they analyse earnings calls, financial reports, and news to summarise risks and opportunities. The library cites 'Large Language Models in Finance', which details an earnings call assistant case study. There, the agent retrieves transcripts, extracts key financial figures, and generates a summary with citations. In legal work, analysts help review contracts and case law. In healthcare, they summarise clinical trials. In everyday applied ML, they can be used for market research, competitive analysis, or due diligence.

How it works in each:
- **LLMs**: The agent is built on an LLM (like GPT or Claude). The LLM does the planning and writing. It decides what tools to call, based on the user's query.
- **Tool use**: The agent has access to tools — a search API, a document reader, a calculator. It calls these tools to gather data. In code, a developer would use a framework like LangChain or LlamaIndex, and define tools as functions the model can invoke.
- **Evidence extraction**: This is often done via retrieval-augmented generation (RAG). The relevant chunks of documents are retrieved based on the query. The agent then reads those chunks and extracts the pieces that answer the question.
- **Synthesis with citations**: The agent writes a report. Each sentence or claim is tagged with a reference to the source document. In practice, this might mean the agent outputs markdown with citations like `[1]`, and the full reference list is included.
- **Agentic systems**: In more advanced systems, the agent might loop: gather, analyse, identify gaps, gather more, until it's satisfied. This is the 'decide, act, observe, repeat' loop.
- **Small models on devices**: Analyst agents that run on phones or laptops are usually constrained. They might only analyse a single document locally, without internet. The LLM is smaller, so the synthesis is less nuanced, but the core structure — gather, extract, cite — remains.

What a developer actually writes: They define a set of tools (e.g., `search_web`, `read_pdf`). They write a prompt for the LLM that describes the task. They set up a loop that calls the LLM, gets a tool call request, executes the tool, feeds the result back, and repeats until the LLM says 'done'. They enforce limits: max iterations, timeout. They log everything for audit.

What breaks when done badly: If the agent doesn't track provenance, it might hallucinate a citation — claim a fact came from a source it never read. If the evidence extraction is too loose, it might pull irrelevant text. If the synthesis step isn't constrained, the agent might 'forget' to cite. If the human review step is skipped, a false conclusion could be acted upon. So developers build in checkpoints: the agent can't finish without a citation for each claim, and a human must approve before the report is sent out.


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Read this on the web: https://basavapurushottam.com/learn-ai/analyst-agents
